def test_svm(train_data,
test_data,
kernel_func=linear_kernel,
lambda_param=.1):
"""
Create an SVM classifier with a specificied kernel_func, train it with
train_data and print the accuracy of model on test_data
:param train_data: a namedtuple including training inputs and training labels
:param test_data: a namedtuple including test inputs and test labels
:param kernel_func: kernel function to use in the SVM
:return: None
"""
svm_model = SVM(kernel_func=kernel_func, lambda_param=lambda_param)
svm_model.train(train_data.inputs, train_data.labels)
train_accuracy = svm_model.accuracy(train_data.inputs, train_data.labels)
test_accuracy = svm_model.accuracy(test_data.inputs, test_data.labels)
if not (train_accuracy is None):
print('Train accuracy: ', round(train_accuracy * 100, 2), '%')
if not (test_accuracy is None):
print('Test accuracy:', round(test_accuracy * 100, 2), '%')
